(1) Field of the Invention
This invention relates to a method of processing a radiographic image with a subject image falling thereon, and radiographic apparatus using thereof. More particularly, this invention relates to an image-processing method that allows dynamic range compression processing, and radiographic apparatus using the method.
(2) Description of the Related Art
Medical institutions are equipped with radiographic apparatus for acquiring a subject image with radiation. When an image is subjected to given image processing, the image has an emphasized structure of such as a blood vessel that falls thereon, which may result in easier diagnosis. Accordingly, the conventional radiographic apparatus may process an acquired image through image processing. Specifically, examples of the image processing that radiation photography apparatus may adopt include dynamic range compression processing that controls distribution of pixel values forming an image. See WO2002/045019, WO2002/045202, Japanese Patent Publications No. H10-75364, H10-171983, H7-38758, and H6-301766.
Description will be given of two methods of performing conventional dynamic range compression processing. Upon performing the dynamic range compression processing to a source image having a subject image falling thereon through a first method, firstly a smooth image is generated having high-frequency components in the source image subtracted from the source image. Then, a reverse image is generated having reversed pixel values that form the smooth image while given weighting is performed in accordance with the pixel values. Where this image is added to the source image, the pixel values that form the source image and express wide distribution from a low pixel value to a high pixel value are offset with the reverse image. Consequently, a dynamic range compression processed image (appropriately, simply referred to as a processed image) is generated having a narrowed distribution range of the pixel values.
However, mere superimposing of both the images causes an irregular processed image. The high-frequency components in the source image are removed upon generation of the reverse image. Consequently, the high-frequency components in the source image are not offset, and relatively excessive and highlighted. This may appear in the processed image as an overshot false image.
According to the conventional method, some thought is added upon generating the reverse image. Specifically, absolute values of high-frequency components in the source image are suppressed prior to subtraction of the high-frequency components from the source image. The reverse image generated in this way has a clear boundary line between dark and bright portions. The reverse image is called a high-frequency component stored-smooth image having a part of the high frequency-components in the source image stored therein. This image is added to the source image, whereby no overshoot appears in the processed image.
Let the foregoing method be a first method. The method is expressed as the following equation:P1=P0+Dinv(P0−ΣLUT)  (1)
Here, P0 indicates a source image, P1 a processed image, ΣLUT high-frequency components in the source image, and Dinv a function for generating a reverse image. Moreover, Dinv (P0−ΣLUT) indicates a high-frequency stored-smooth image.
Moreover, dynamic range compression process includes, as a second method, a method of simply converting pixel values of the source image to generate a conversion image. According to this method, however, not only low-frequency components but also high-frequency components in the source image are removed. Particularly in a portion of the source image with a certain pixel value mentioned later, more high-frequency components are removed to cause a smoothed image in this portion of the processed image. Consequently, the conversion image has deteriorated contrast.
The extent of removing the high-frequency components upon conversion of the source image is determined in accordance with a translation table used when a pixel value of the source image is converted into a pixel value of the processed image. This translation table is table data having a relationship between an input value expressing the pixel value of the source image and an output value expressing the pixel value of the processed image. When seen the input values in the translation table in order from the lower one to the higher one, it is found that the translation table has a portion where the output value hardly varies upon variation of the input values (a low variation portion) and a portion where the output value largely varies upon variation of the input values (a high variation portion.) Moreover, the high-frequency components are remarkably removed upon conversion of the value in the low variation portion in the translation table. The high-frequency components are hardly removed upon conversion of the value in the high variation portion in the translation table.
In view of the state as above, the conventional method adopts a configuration of adding the high-frequency components in the source image to the conversion image. As mentioned above, the high-frequency components in the source image are readily removed from the processed image as the differentiation values are lower in the translation table. Accordingly, such weighting is performed that more high-frequency components in the source image are added as the reciprocal of the differentiation value is higher upon adding of the high-frequency components in the source image to the conversion image. In so doing, the processed image to be finally generated includes the high-frequency components, which realizes a conversion image having maintained contrast.
The second method is expressed as the following equation:P1−Dconv(P0)+(1/Dconv′(P0))×ΣLUT  (2)
Here, P0 indicates a source image, P1 a processed image, ΣLUT high-frequency components in the source image, and Dconv a function specified by the translation table.
However, a problem arises that the conventional first method cannot realize proper characteristic of dynamic range compression processing. Where the characteristic of dynamic range compression (compression characteristics) is modified, Dinv in Equation (1) is to be modified. As is apparent from Equation (1), the function Dinv operates on the image having the high-frequency components already contained therein. Consequently, where the intensity of dynamic range compression falls short in the high-frequency components, it is difficult to modify the function Dinv for adjusting the compression characteristics only in the high-frequency components. As noted above, the function Dinv operates on the image having the low and high-frequency components in the source image mixed therein. Accordingly, the compression characteristics is not modified independently among the low and high-frequency components. Thus, only the dynamic range compression processed image is acquired having decreased visibility.
Moreover, the conventional second method includes an index Dconv′ (P0) as weighting used in addition of the high-frequency components. The source image P0 contains noise components regardless of the subject image. According to the conventional second method, the noise components are considered in weighting used in addition of the high-frequency components. Accordingly, the processed image to be finally generated is disturbed by the noise components in the source image P0.